Programs Scholarships Curriculum Admission Courses Research Projects Internships Undergraduate Advising Team Articulation Pathways Postgraduate MPhil/PhD in Life Science PhD Dual-degree Programme MSc in Biotechnology MSc in Drug Regulatory Affairs and Policy (DRAP) Admission Courses Summer Camp Integrated BSc-MSc Pathway QA at HKUST

Dr. Hao Chen received the Ph.D. degree from The Chinese University of Hong Kong (CUHK) in 2017. He leads the Smart Lab focusing on developing trustworthy AI for healthcare. He has 100+ publications (Google Scholar Citations 23K+, h-index 63) in MICCAI, IEEE-TMI, MIA, CVPR, AAAI, Nature Communications, Radiology, Lancet Digital Health, Nature Machine Intelligence, JAMA, etc. He also has rich industrial research experience (e.g., Siemens), and holds a dozen of patents in AI and medical image analysis. He received several premium awards such as Asian Young Scientist Fellowship in 2023, MICCAI Young Scientist Impact Award in 2019, Forbes China 30 under 30 and several best paper awards. He serves as the Associate Editor of multiple journals including IEEE Transactions on Neural Networks and Learning Systems, Journal of Biomedical and Health Informatics, Neurocomputing, Computerized Medical Imaging and Graphics, Medical Physics, etc. He serves as the Program Committee of multiple international conferences including Area Chair of MICCAI 2021-2023, ACM MM 2024, MIDL 2022-2023, CVPR 2023 and SPC of AAAI 2022, etc. He also led the team winning 15+ medical grand challenges.

Trustworthy Artificial Intelligence for Healthcare

Artificial intelligence (AI), especially deep learning with large-scale training datasets, has dramatically advanced the recognition performance in many domains including speech recognition, visual recognition and natural language processing. Despite its breakthroughs in above domains, its application to healthcare remains yet to be further explored, where large-scale fully and high-quality annotated datasets are not easily accessible. We aim to develop trustworthy AI for healthcare, including large-scale foundation models, multimodal data integration for precision oncology and explainable AI (XAI), with versatile applications to disease diagnosis and prognosis, human-machine collaboration, etc.

Xiang H, Xiao Y, Li F, Li C, Liu L, Deng T, Yan C, Zhou F, Wang X, Ou J, Lin Q, Hong R, Huang L, Luo L, Lin H, Lin X, Chen H . OvcaFinder: Development and Validation of an Interpretable Model Integrating Multimodal Information for Improving Ovarian Cancer Diagnosis: A Retrospective Study. Nature Communications 2024 .

Zhang Y, Xu Y, Chen J, Xie F, Chen H . Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction. International Conference on Learning Representations ( ICLR ) 2024.

Xu Y, Chen H . Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction. International Conference on Computer Vision ( ICCV ) 2023 .

Zhou F, Chen H . Cross-Modal Translation and Alignment for Survival Analysis. ICCV 2023 .

Ma J, Chen H . Efficient Supervised Pretraining of Swin-transformer for Virtual Staining of Microscopy Images. IEEE Transactions on Medical imaging ( TMI ) 2023 .